Research Article Real-Time Human Ear Detection Based on the Joint of Yolo and RetinaFace Huy Nguyen Quoc and Vinh Truong Hoang Ho Chi Minh City Open University, 35-37 Ho Hao Hon Street, Ward Co Giang, District 1, Ho Chi Minh City, Vietnam CorrespondenceshouldbeaddressedtoVinhTruongHoang;vinh.th@ou.edu.vn Received 21 September 2021; Revised 14 October 2021; Accepted 18 October 2021; Published 8 November 2021 AcademicEditor:BaltazarAguirreHernandez Copyright © 2021 Huy Nguyen Quoc and Vinh Truong Hoang. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Biometrictraitsgraduallyprovedtheirimportanceinreal-lifeapplications,especiallyinidentificationfield.Amongtheavailable biometrictraits,theuniqueshapeofthehumanearhasalsoreceivedloadsofattentionfromscientiststhroughtheyears.Hence, numerousear-basedapproacheshavebeenproposedwithpromisingperformance.Withthesemethods,plentyproblemscanbe solvebythedistinctivenessofearfeatures,suchasrecognizinghumanwithmaskordiagnoseear-relateddiseases.Asacomplete identificationsystemrequiresaneffectivedetectorforreal-timeapplication,andthecurrentrichnessandvarietyofeardetection algorithmsarepoorduetothesmallandcomplexshapeofhumanears.Inthispaper,weintroduceanewhumaneardetection pipelinebasedontheYOLOv3detector.Awell-knownfacedetectornamedRetinaFaceisalsoaddedinthedetectionsystemto narrowtheregionsofinterestandenhancetheaccuracy.eproposedmethodisevaluatedonanunconstraineddataset,which shows its effectiveness. 1. Introduction Identificationalwaysholdsanessentialroleinourdailylives, such as information security, banking transactions, and e-commerce. With the development of computer vision, most identification systems are now based on biometric traits. However, due to the COVID-19 pandemic, people havetowearmasksorprotectivegearsallthetimeinpublic. isissuelimitsthepossibilityofseveralbiometricpatterns, includingface,iris,andfingerprints.erefore,weproposed toapplythehumaneartosubstitutetheavailablebiometric traitsinidentificationtasks.Asahumanhearingorgan,the earshavebeenprovedtobeasdistinctiveasotherbiometric patterns. Specifically, parts such as the helix, the antihelix, the tragus, the antitragus, and the fossa have formed nu- merous curves during ear development [1]. ese curves createtheouteroftheear,whichisalsocalledthepinna,and providetheuniquenessofthehumanear[2].Evenearsfrom the same person still have several differences. With these studies, the first human ear identification system was pre- sentedbyManuelZimberoffin1963.Afterthat,loadsofear- basedapproacheshavebeenproposedinordertoreplacethe common biometric traits with the human ear in several computervisiontasksorjustsimplycombiningthefeatures of the human ear with other biometric patterns to enhance theperformance.Forexample,Alshazlyetal.combineddeep learning and transfer learning models to analyze and rec- ognize human ears [3]. Hassaballah et al. extracted features fromearimageusingtheLBPdescriptoranditsvariantsfor classification [4]. In 2020, Alshazly et al. proposed a neural network to recognize unconstrained ear images [5]. In that year, Ganapathi et al. presented a geometric feature for 3D ear recognition [6]. Several ear comparative studies and surveys were also made by Pflug et al. for research purpose [7, 8]. ese approaches allow us to build multiple appli- cationstosolveear-relatedtasks.Currently,oneofthemost urgent and essential problems which is face with mask recognitioncanbesolvedwitheardetectionbecauseearsare not occluded when wearing mask. Ear recognition is also helpful when identifying person from other angles which is very useful for large-scale recognition tasks and cameras with fixed angle. Furthermore, ear detection can be applied Hindawi Complexity Volume 2021, Article ID 7918165, 11 pages https://doi.org/10.1155/2021/7918165